CrowdStart: Warming up cold-start items using crowdsourcing
- Authors
- Hong, Dong-Gyun; Lee, Yeon-Chang; Lee, Jongwuk; Kim, Sang-Wook
- Issue Date
- Dec-2019
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Collaborative filtering; New item recommendation; Crowdsourcing
- Citation
- EXPERT SYSTEMS WITH APPLICATIONS, v.138, pp.1 - 15
- Indexed
- SCIE
SCOPUS
- Journal Title
- EXPERT SYSTEMS WITH APPLICATIONS
- Volume
- 138
- Start Page
- 1
- End Page
- 15
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/146713
- DOI
- 10.1016/j.eswa.2019.07.030
- ISSN
- 0957-4174
- Abstract
- The cold-start problem is one of the critical challenges in personalized recommender systems. A lot of existing work has been studied to exploit a user-item rating matrix as well as additional information for users/items, e.g., user profiles, item contents, and social relationships among users. However, because existing work is primarily biased to the auxiliary information for users/items, it is difficult to identify various and reliable item neighbors that are relevant to cold-start items. To alleviate this limitation, we propose a new crowd-enabled framework, called CrowdStart, which is an integrated human-machine approach for new item recommendation. The main contributions of the CrowdStart framework are twofold: (1) To find various and reliable item neighbors for new items, we design two-step crowdsourcing tasks that harness not only machine-only algorithms but also the knowledge of crowd workers (including a few experts and a large number of non-expert workers in a crowdsourcing platform). (2) We develop a novel hybrid model to exploit the user-item rating matrix, the content information about items, and the crowd-based item neighbors from human knowledge into new item recommendation. To evaluate the effectiveness of the CrowdStart framework, we conduct extensive experiments including both a user study and simulation tests. Through the empirical study, we found that the CrowdStart framework provides relevant, diverse, reliable, and explainable crowd-based neighbors for new items and the crowd-based neighbors are meaningful for improving the accuracy of new item recommendation. The datasets and detailed experimental results are available at https://goo.gl/1iXTUE.
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